parametrization-clean-cdaksha 1.0.0.2

ReaxFF parameter optimization scheme using generational genetic algorithm and neural networks.

ReaxFF Parametrization with Clean Architecture

Contains Python files and Bash scripts as a basis for automated ReaxFF parametrization. Uses the genetic algorithm (GA) algorithm as well as an artificial neural network (ANN) to optimize a ReaxFF parameter set. Note that this version is refactored to attempt to comply with Robert Martin's Clean Architecture guidelines. Using the generational genetic algorithm and neural net (the latter only if enabled), runs one generation, awaiting submission of standalone ReaxFF optimizations.

Getting Started

The project files are available as a GitHub repository here. The project can also be accessed through PyPi here; the corresponding pip installation command is

$pip install parametrization-clean-cdaksha

If you don't have pip and/or Python installed, then this guide may prove helpful in performing a basic setup. If, for whatever reason, a distribution manager such as pip or conda is not available, then the required packages for running the application are shown in requirements/prod.txt.

The current implementation supports a command-line interface with usage

$python cli.py --g GENERATION_NUMBER --t TRAINING_PATH --p POPULATION_PATH

where GENERATION_NUMBER is the current generation number in the generational genetic algorithm, TRAINING_PATH is the file path location of the reference ReaxFF training set files, and POPULATION_PATH is the location at which the user wishes the generational genetic algorithm files to be outputted.

If GENERATION_NUMBER = 1, then the first population is initialized, whose ReaxFF optimizations can then be submitted for evaluation of the parameters. If GENERATION_NUMBER > 1, the previous generation's data is read from POPULATION_PATH, and classic genetic operators are applied to generate the next generation and output to the POPULATION_PATH once again, after which the corresponding ReaxFF optimizations may be submitted.

To automate the generational genetic algorithm, an example slurm script is provided in the scripts directory. This allows concurrent submission of ReaxFF optimizations and continuation of the generational genetic algorithm until a threshold, defined by a maximum generation number, is reached.

Note that several options are provided for potential mutation, crossover, etc., algorithms that the user may use. Reasonable defaults based on the literature are provided, but they are easy to override by defining a custom config.json file in the project root directory.

Prerequisites

Project relies on usage of pip for installing required dependencies. Additionally, standalone ReaxFF is required to run the optimizations for the files that are created. Note that reference ReaxFF training files for the system at hand are required. At the very least, the training set directory must contain

training_files
│---ffield
│---geo
│---params
│---control
│---trainset.in
│---fort.99

Note that iopt files are dynamically created with a single line entry, 0, to instruct ReaxFF not to use the "manual" ReaxFF parameter optimization scheme: successful one parameter parabolic extrapolation (SOPPE).

Currently, fort.99 is required in the training set directory to retrieve DFT energies and weights in the beginning.

Running the tests

The project can easily be tested by running the test suite through

$tox

in the project root after installation. Note that TensorFlow is used in building the neural network. If TensorFlow is unavailable for installation, then the tests corresponding to the neural network will not run. Code coverage can be checked by running

$py.test --cov-report term-missing --cov=parametrization_clean

To check for conformation to PEP standards, one can use

flake8

in the project root directory.

Authors

• Chad Daksha - Initial work - cdaksha

License

This project is licensed under the MIT License - see the LICENSE.md file for details